Triple
T3376579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hennepin County |
E71080
|
entity |
| Predicate | state |
P87
|
FINISHED |
| Object | Minnesota |
E33799
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Minnesota | Statement: [Hennepin County, state, Minnesota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minnesota Context triple: [Hennepin County, state, Minnesota]
-
A.
Minnesota
chosen
Minnesota is a U.S. state known for its numerous lakes, cold winters, and vibrant cultural and economic centers like Minneapolis–Saint Paul.
-
B.
D. Minn.
D. Minn. is the standard legal abbreviation for the United States District Court for the District of Minnesota, a federal trial court within the Eighth Circuit.
-
C.
Minnesota and Wisconsin
Minnesota and Wisconsin are two neighboring U.S. states in the Upper Midwest, known for their abundant lakes, forests, and shared border along the upper Mississippi River.
-
D.
Iowa
Iowa is a Midwestern U.S. state known for its extensive agriculture, especially corn and soybean production, and its role in national politics through the Iowa caucuses.
-
E.
Wisconsin
Wisconsin is a U.S. state in the Upper Midwest known for its dairy industry, Great Lakes shorelines, and mix of rural landscapes and industrial cities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85a7f80c8190a05e43013f298942 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2e776508190bc123fb17b36f062 |
completed | March 8, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bba09b481908f448aa1e6fe60be |
completed | March 12, 2026, 11:26 p.m. |
Created at: March 8, 2026, 3:13 p.m.